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UNSEEN: Enhancing Dataset Pruning from a Generalization Perspective
Furui Xu, Shaobo Wang, Jiajun Zhang +3
The growing scale of datasets in deep learning has introduced significant computational challenges. Dataset pruning addresses this challenge by constructing a compact but informati…
ImagebindDC: Compressing Multi-modal Data with Imagebind-based Condensation
Yue Min, Shaobo Wang, Jiaze Li +5
Data condensation techniques aim to synthesize a compact dataset from a larger one to enable efficient model training, yet while successful in unimodal settings, they often fail in…
CircuitSeer: Mining High-Quality Data by Probing Mathematical Reasoning Circuits in LLMs
Shaobo Wang, Yongliang Miao, Yuancheng Liu +3
Large language models (LLMs) have demonstrated impressive reasoning capabilities, but scaling their performance often relies on massive reasoning datasets that are computationally…
Socratic-Zero : Bootstrapping Reasoning via Data-Free Agent Co-evolution
Shaobo Wang, Zhengbo Jiao, Zifan Zhang +6
Recent breakthroughs in large language models (LLMs) on reasoning tasks rely heavily on massive, high-quality datasets-typically human-annotated and thus difficult to scale. While…
SpeCa: Accelerating Diffusion Transformers with Speculative Feature Caching
Jiacheng Liu, Chang Zou, Yuanhuiyi Lyu +4
Diffusion models have revolutionized high-fidelity image and video synthesis, yet their computational demands remain prohibitive for real-time applications. These models face two f…
Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching
Zhixin Zheng, Xinyu Wang, Chang Zou +2
Diffusion transformers have gained significant attention in recent years for their ability to generate high-quality images and videos, yet still suffer from a huge computational co…
Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs
Yufa Zhou, Shaobo Wang, Xingyu Dong +7
Directly training Large Language Models (LLMs) for Multi-Agent Systems (MAS) remains challenging due to intricate reward modeling, dynamic agent interactions, and demanding general…
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
Zekai Li, Xinhao Zhong, Samir Khaki +49
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance co…